TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision

Video copy localization aims to precisely localize all the copied segments within a pair of untrimmed videos in video retrieval applications. Previous methods typically start from frame-to-frame similarity matrix generated by cosine similarity between frame-level features of the input video pair, and then detect and refine the boundaries of copied segments on similarity matrix under temporal constraints. In this paper, we propose TransVCL: an attention-enhanced video copy localization network, which is optimized directly from initial frame-level features and trained end-to-end with three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for similarity matrix generation, and a temporal alignment module for copied segments localization. In contrast to previous methods demanding the handcrafted similarity matrix, TransVCL incorporates long-range temporal information between feature sequence pair using self- and cross- attention layers. With the joint design and optimization of three components, the similarity matrix can be learned to present more discriminative copied patterns, leading to significant improvements over previous methods on segment-level labeled datasets (VCSL and VCDB). Besides the state-of-the-art performance in fully supervised setting, the attention architecture facilitates TransVCL to further exploit unlabeled or simply video-level labeled data. Additional experiments of supplementing video-level labeled datasets including SVD and FIVR reveal the high flexibility of TransVCL from full supervision to semi-supervision (with or without video-level annotation). Code is publicly available at https://github.com/transvcl/TransVCL.Comment: Accepted by the Thirty-Seventh AAAI Conference on Artificial Intelligence(AAAI2023

Action analysis and control strategy for rat robot automatic navigation

A rat robot is an animal robot, where a rat is connected to a machine system via a brain-computer interface. Electrical stimuli can be generated by the machine system and delivered to the rat\u27s brain to control its behavior. The sensory capacity and flexible motion ability of rat robots highlight their potential advantages over mechanical robots. There are two challenges of rat robot automatic navigation. The first challenge is to recognize the action status of the rat robot, which is an essential feedback for determining the stimuli/instructions for it to accomplish certain movements. The second challenge is the design of the automatic instruction model that steers the rat robot to perform navigation. Due to inherent characteristics and instincts of the rats, the controlling strategy of the rat robots is different from mechanical robots. In this thesis, we propose a new idea for analyzing the action states of the rat robot. A miniature camera is mounted on the back of the rat robot and the egocentric video captured by the camera is used to analyze its action. We propose two action analysis methods. The first method is based on an optical flow algorithm and the second method incorporates deep neural networks. We propose two automatic instruction models. The first model learns from manual control data to mimic the human controlling process, and the second model issues instructions according to human experts\u27 knowledge. We build a rat robot and apply these models to enable it to navigate in different scenes automatically. In order to produce more accurate optical flow estimation, we propose a row convolutional long short-term memory (RC-LSTM) network to model the spatial dependencies among image pixels. Our RC-LSTM network is integrated with Convolutional Neural Networks and achieves competitive accuracy. To analyze potentially more complex actions from the egocentric videos, we extend our deep neural networks used for rat states analysis to be a two-stream architecture. A spatial attention network is incorporated to help our model to focus on the relevant spatial regions to recognize actions. Our model is evaluated on two egocentric action recognition datasets and achieves competitive performance

A Novel Pallet Detection Method for Automated Guided Vehicles Based on Point Cloud Data

Automated guided vehicles are widely used in warehousing environments for automated pallet handling, which is one of the fundamental parts to construct intelligent logistics systems. Pallet detection is a critical technology for automated guided vehicles, which directly affects production efficiency. A novel pallet detection method for automated guided vehicles based on point cloud data is proposed, which consists of five modules including point cloud preprocessing, key point extraction, feature description, surface matching and point cloud registration. The proposed method combines the color with the geometric features of the pallet point cloud and constructs a new Adaptive Color Fast Point Feature Histogram (ACFPFH) feature descriptor by selecting the optimal neighborhood adaptively. In addition, a new surface matching method called the Bidirectional Nearest Neighbor Distance Ratio-Approximate Congruent Triangle Neighborhood (BNNDR-ACTN) is proposed. The proposed method overcomes the problems of current methods such as low efficiency, poor robustness, random parameter selection, and being time-consuming. To verify the performance, the proposed method is compared with the traditional and modified Iterative Closest Point (ICP) methods in two real-world cases. The results show that the Root Mean Square Error (RMSE) is reduced to 0.009 and the running time is reduced to 0.989 s, which demonstrates that the proposed method has faster registration speed while maintaining higher registration accuracy

Landscape Effects on the Abundance of Apolygus lucorum in Cotton Fields

Resource-continuity over spatial and temporal scales plays a central role in the population abundance of polyphagous pests in an agricultural landscape. Shifts in the agricultural land use in a region may alter the configuration of key resource habitats, resulting in drastic changes in pest abundance. Apolygus lucorum (Meyer-Dür) (Hemiptera: Miridae) is a pest of cotton in northern China that has become more serious in recent years following changes in the region’s cropping systems. However, no evidence from the landscape perspective has yet been gathered to account for the increasing population of A. lucorum in China. In this study, we investigated the effects of landscape composition on the population abundance of A. lucorum in cotton fields in July and August of 2016, respectively. We found that increased acreage planted to cotton actually had a negative effect on the abundance of A. lucorum, while planting of other crops (e.g., vegetables, soybean, and peanut) was positively associated with the mirid’s population abundance in cotton fields. Maize production only displayed a positive effect on population abundance in August. Our results suggested that the decreasing of cotton area may weaken the trap-kill effect on A. lucorum, and the extension of other crops and maize potentially enhance the continuity of resources needed by A. lucorum. Combined effects of these two aspects may promote an increased population density of A. lucorum in the agriculture district. In the future, when possible, management strategies in key regional crops should be coordinated to reduce resource continuity at the landscape or area-wide scale to lower A. lucorum populations across multiple crops

Perennial woodlands benefit parasitoid diversity, but annual flowering fallows enhance parasitism of wheat aphids in an agricultural landscape

Agriculture intensification poses serious threats to natural enemy biodiversity and associated ecological services. The conservation or reestablishment of semi-natural habitats is used to counteract negative effects of agriculture intensification on natural enemies. Understanding specific functions of different habitats for natural enemies from a landscape perspective is an important step needed for the development of sustainable agriculture. Here, focusing on parasitoids of wheat aphids, we examined effects of the proportion and connectivity of two main semi-natural habitats (woodlands and fallows) present in landscapes, measured within circular buffer radii of 0.5, 1.0, 1.5, and 2.0 km around sampling sites, on parasitoid (mummy) density, biodiversity (Shannon diversity) and associated services (parasitism rate) in 35 wheat fields. We also compared local vegetation communities of these two semi-natural habitats to test whether plant characteristics can shed light on the potential mechanisms driving parasitoids responses to different landscape habitats. We found that the parasitoid diversity was much higher in landscapes dominated by woodlands, while fallows in the landscape promoted parasitoid density and parasitism. Woodlands connectivity at larger scales (such as 1.5 or 2.0 km) displayed positive effects on parasitoid activities, fallows connectivity at the smaller scale (0.5 km) had a positive effect on the hyperparasitism rate. In terms of vegetation characteristics, fallows provided more flowering plants and floral resources, while woodlands suffered less disturbance across years. Local vegetation composition of the semi-natural habitats indeed help explain their different effects on parasitoids at larger landscape scales. We suggested that future research should investigate the role of different types of semi-natural habitats. Conservation management should combine different habitats, such as perennial and annual habitats, to promote the functional complementarity for beneficial organisms. Based on results from local vegetation survey, we also suggested native flowering plants such as Capsella bursa-pastoris L., Lagopsis supina Steph., and Calystegia hederacea Wall. in fallows could be used as functional plants to conserve wheat aphid parasitoids

Perennial Flowering Plants Sustain Natural Enemy Populations in Gobi Desert Oases of Southern Xinjiang, China

Natural habitats play crucial roles in biodiversity conservation and shape the delivery of ecosystem services in farming landscapes. By providing diverse resources to foraging natural enemies, they can equally enhance biological pest control. In this study, we described the plant community and foliage-dwelling invertebrate predators within non-crop habitats of the Gobi Desert oases in southern Xinjiang, China. We assessed whether plant-related variables (i.e., species identity, flowering status) and herbivore abundance affect natural enemy identity and abundance. A total of 18 plant species belonging to 18 genera and 10 families were commonly encountered, with Apocynum pictum (Apocynaceae), Phragmites communis (Poaceae), Karelinia caspia (Asteraceae), and Tamarix ramosissima (Tamaricaceae) as the dominant species. Certain plant species (P. communis) primarily provide shelter, while others offer (floral, non-floral) food resources or alternative prey. Predatory ladybeetles and spiders were routinely associated with these plants and foraged extensively within adjacent field crops. Plant traits and herbivore abundance explained up to 44% (3%–44%) variation in natural enemy community and exhibited consistent, year-round effects. Among all plant species, A. pictum consistently had a significantly higher abundance of resident natural enemies, except for August 2019. Our study underlines how perennial flowering plants, such as A. pictum, are essential to sustain natural enemy communities and related ecosystem services in arid settings. This work not only informs sustainable pest management initiatives but also shows how non-crop habitats at the periphery of agricultural fields underpin ecological resilience under adverse climatic conditions

Split luciferase-based biosensors for characterizing EED binders

The EED (embryonic ectoderm development) subunit of the Polycomb repressive complex 2 (PRC2) plays an important role in the feed forward regulation of the PRC2 enzymatic activity. We recently identified a new class of allosteric PRC2 inhibitors that bind to the H3K27me3 pocket of EED. Multiple assays were developed and used to identify and characterize this type of PRC2 inhibitors. One of them is a genetically encoded EED biosensor based on the EED[G255D] mutant and the split firefly luciferase. This EED biosensor can detect the compound binding in the transfected cells and in the in vitro biochemical assays. Compared to other commonly used cellular assays, the EED biosensor assay has the advantage of shorter compound incubation with cells. The in vitro EED biosensor is much more sensitive than other label-free biophysical assays (e.g. DSF, ITC). Based on the crystal structure, the DSF data as well as the biosensor assay data, it's most likely that compound-induced increase in the luciferase activity of the EED[G255D] biosensor results from the decreased non-productive interactions between the EED subdomain and other subdomains within the biosensor construct. This new insight of the mechanism might help to broaden the use of the split luciferase based biosensors

Single-Domain Antibody-Based TCR-Like CAR-T: A Potential Cancer Therapy

Retargeting the antigen-binding specificity of T cells to intracellular antigens that are degraded and presented on the tumor surface by engineering chimeric antigen receptor (CAR), also named TCR-like antibody CAR-T, remains limited. With the exception of the commercialized CD19 CAR-T for hematological malignancies and other CAR-T therapies aiming mostly at extracellular antigens achieving great success, the rareness and scarcity of TCR-like CAR-T therapies might be due to their current status and limitations. This review provides the probable optimized initiatives for improving TCR-like CAR-T reprogramming and discusses single-domain antibodies administered as an alternative to conventional scFvs and secreted by CAR-T cells, which might be of great value to the development of CAR-T immunotherapies for intracellular antigens